Enhancing Health Equity by Predicting Missed Appointments in Health Care: Machine Learning Study
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JMIR Publications
Abstract
BACKGROUND: The phenomenon of patients missing booked appointments without canceling them-known as Did Not Show (DNS), Did Not Attend (DNA), or Failed To Attend (FTA)-has a detrimental effect on patients' health and results in massive health care resource wastage. OBJECTIVE: Our objective was to develop machine learning (ML) models and evaluate their performance in predicting the likelihood of DNS for hospital outpatient appointments at the MidCentral District Health Board (MDHB) in New Zealand. METHODS: We sourced 5 years of MDHB outpatient records (a total of 1,080,566 outpatient visits) to build the ML prediction models. We developed 3 ML models using logistic regression, random forest, and Extreme Gradient Boosting (XGBoost). Subsequently, 10-fold cross-validation and hyperparameter tuning were deployed to minimize model bias and boost the algorithms' prediction strength. All models were evaluated against accuracy, sensitivity, specificity, and area under the receiver operating characteristic (AUROC) curve metrics. RESULTS: Based on 5 years of MDHB data, the best prediction classifier was XGBoost, with an area under the curve (AUC) of 0.92, sensitivity of 0.83, and specificity of 0.85. The patients' DNS history, age, ethnicity, and appointment lead time significantly contributed to DNS prediction. An ML system trained on a large data set can produce useful levels of DNS prediction. CONCLUSIONS: This research is one of the very first published studies that use ML technologies to assist with DNS management in New Zealand. It is a proof of concept and could be used to benchmark DNS predictions for the MDHB and other district health boards. We encourage conducting additional qualitative research to investigate the root cause of DNS issues and potential solutions. Addressing DNS using better strategies potentially can result in better utilization of health care resources and improve health equity.Description
Keywords
appointment nonadherence, data analytics, decision support system, Did Not Attend, Did Not Show, health care operation, health equity, machine learning, patients no-show, prediction, predictive modeling, Did Not Attend, Did Not Show, appointment nonadherence, data analytics, decision support system, health care operation, health equity, machine learning, patients no-show, prediction, predictive modeling, 4206 Public Health, 42 Health Sciences, Health Services, Clinical Research, 3 Good Health and Well Being, 4203 Health services and systems
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JMIR Medical Informatics, ISSN: 2291-9694 (Print); 2291-9694 (Online), JMIR Publications, 12, e48273-. doi: 10.2196/48273
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©Yi Yang, Samaneh Madanian, David Parry. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 12.01.2024.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.